This research proposal outlines the development of a foundation model for a double-armed kitchen robot capable of performing diverse tasks through a combination of Vision-Language-Action (VLA) modeling and diffusion policy techniques. The model will be designed to handle various grabbing tasks and adapt to different tools, with the ability to learn new tasks in a few-shot manner.
Foundation Model Architecture
Vision-Language-Action (VLA) Component
The core of our foundation model will be a VLA system, which integrates visual perception, language understanding, and action generation[1]. This component will be responsible for high-level planning and task comprehension.
Diffusion Policy Integration
We will incorporate a diffusion policy into our VLA model to handle low-level interactions and precise movements[1]. This integration will allow for:
- Handling of high-dimensional action spaces
- Management of multimodal action distributions
- Improved training stability
Hybrid Control Method
Our foundation model will employ a hybrid control method that combines the strengths of both VLA and diffusion models:
- VLA for language-commanded high-level planning
- Diffusion model for low-level interactions and precision
Switching Mechanism
We will implement a switching signal to enable event-based transitions between the VLA and diffusion models[1]. This will allow for seamless coordination between high-level planning and precise execution.
Tool Adaptation
To enable the robot to use various tools (e.g., whisks, spatulas), we will implement:
- A tool recognition module within the vision component
- A tool-specific action space mapping in the diffusion policy
- A language model extension to understand tool-related instructions
Few-Shot Learning Capability
To achieve few-shot learning of new tasks, we will incorporate the following techniques:
Diffusion Transformer Architecture
We will implement a Diffusion Transformer architecture, which has shown promise in generalist robot policy learning[4]. This architecture will allow for:
- Efficient denoising of continuous actions
- Improved handling of diverse action spaces
- Better generalization across different tasks
In-Context Conditioning
The Diffusion Transformer will utilize in-context conditioning, allowing it to adapt to new tasks with minimal examples[4].
TinyVLA Integration
We will incorporate elements from the TinyVLA framework to improve data efficiency and inference speed[2]. This will include:
- Initializing the policy backbone with robust, high-speed multimodal models
- Integrating a diffusion policy decoder during fine-tuning
Meta-Learning Framework
To further enhance few-shot learning capabilities, we will implement a meta-learning framework that allows the model to quickly adapt to new tasks with minimal examples.
Training and Evaluation
Training Data
We will use a combination of:
- Large-scale cross-embodiment datasets (e.g., Open X-Embodiment Dataset)[4]
- Task-specific kitchen manipulation data
Evaluation Metrics
We will assess the model’s performance using:
- Success rates for various kitchen tasks
- Generalization capabilities to new views and environments[5]
- Few-shot learning efficiency for novel tasks
Conclusion
This research proposal outlines a cutting-edge approach to developing a foundation model for a double-armed kitchen robot. By combining VLA modeling, diffusion policies, and advanced few-shot learning techniques, we aim to create a versatile and adaptable system capable of performing a wide range of kitchen tasks while easily learning new skills with minimal examples.
Citations:
[1] Vision-Language-Action Model and Diffusion Policy Switching Enables Dexterous Control of an Anthropomorphic Hand
[2] Vision-Language-Action Model and Diffusion Policy Switching Enables Dexterous Control of an Anthropomorphic Hand | AI Research Paper Details
[3] https://diffusion-policy.cs.columbia.edu
[4] https://openreview.net/pdf?id=PvvXDazPMs
[5] https://openreview.net/pdf/9ac0b98a230a3ae07dbc5ece257b8f7484e528eb.pdf
[6] https://tiny-vla.github.io
[7] https://openvla.github.io
[8] [Paper Review] OpenVLA: An Open-Source Vision-Language-Action Model